Autonomic neuropathy (AN) generally describes the affection of the small C nerve fibers, which impairs the autonomic nervous system. In diabetes, its symptoms include either extreme hot or cold sensation across the legs, acute pain, and low sweating capacity or even the absence of it. Furthermore, this set of symptoms usually leads to the so called "diabetic foot". Multiple diagnostic tools are available on the market, such as the monofilament 10g, the quantitative axon reflex test (QSART), the thermoregulatory sweat testing (TST) and the Neuropad (SánzCorbalán et al., 2011, Papanas et al., 2005). Caring strategies have also been exhaustively developed and promoted to improve the life quality and expectancy of those who suffer it. Still, 50% of diabetics will suffer from some type of neuropathy, responsible for 20% of ER visits. It has also been demonstrated that the risk of amputation triples when preceded by an ulceration in diabetics. In addition, foot ulcerations cause 80% of the non-traumatic amputations worldwide, leaving a life expectancy of 2 to 5 years (Boulton, Cavanagh and Rayman 2007, De Alcalá 2005). The electrical conductance of the skin and its behavior, normally referred to as electrodermal activity (EDA) or as galvanic skin response (GSR) (González-Correa 2018 a), is typically divided into low and high frequency components. The former is usually named skin conductance level (SCL) and the latter, skin conductance response (SCR). High frequency responses are also classified depending on whether they are produced by an intended stimulus (event-related SCR) or not (nonspecific SCR) (Braithwaite et al., 2013). EDA has been used to measure stress (Najstrom and Jansson 2007; Reinhardt et at., 2012; Sierra, et al., 2011), modulation during exercise (Posada Quintero et al., 2018) or to evaluate user experience (Rico-Olarte et al., 2018), just to name a few. For a review in Spanish on the topic, please refer to the study of Mojica-Londono (2017), or in English to Moncada and De la Cruz (2011). Earlier researches have studied the EDA as a method for monitoring diabetes or its correlation with AN in different regions of the body (Khalfallah et al., 2012; Deanfield et al., 1980; Colmenares Guillén et al., 2015; Li at al., 2004; Nazhel et al., 2002; Rivera Farina et al., 2009; Vlckova et al., 2016; Wang et al., 2008). From the aforementioned studies, only Vlckova, Srotova and Bednarik (2016) found no correlation. More recently, Mohanraj and Narayanan (2016) found a correlation between type 2 diabetes and low GSR in males. Finally, Ionescu-Tirgoviste et al. (2018) found significant differences in the electrical activity of type 2 diabetics in certain regions of the trunk. Colombian contributions relate to the usage of bio-impedance, including EDA, as a clinical diagnostic tool (C. A. González-Correa 2018, C. H. González-Correa 2018) and novel methods to analyse EDA based on frequency-domain features (Posada Quintero et al., 2016). The aims of this study were to independently study the correlation between EDA and AN among diabetic patients, addressing new variables, and to propose a novel low-cost method to diagnose the latter.

Participants were patients of the diabetic foot care program conducted between September and December 2016 by the medical service of the University of Cauca, in Popayan, Colombia. The study was approved by the Ethics Committee of the same institution. Patients were classified based on the guidelines given by Boulton et al., (2007), De Alcalá (2005), and on the expertise and physical examination of their feet, performed by the head nurse of the program. Clinical data, including gender, age, and time suffering from diabetes, were collected. Therefore 3, groups were formed: the reference group, namely participants without diabetes, diabetics out of risk, and diabetics either at risk of diabetic foot or with already visible complications (ulcerations or toes amputation).

22 subjects participated in the final study: 5 of them were part of the control group (group 1), 6 were classified as out of risk diabetics (group 2) and 11 as at risk (group 3). 3 subjects were excluded from the final analysis due to erroneous data caused by a device failure at the moment of the test. Age correlated well with classification between groups 2 and 3 (4,4 ± 4,43 years vs 11,64 ± 7,66 years, p < 0,05).

Differences in reaction to stimuli among subjects were found. In Figure 2, a higher response towards loud unexpected noises is evident (stimuli correspondent to vertical dashed lines 1, 2 and 3), followed by lower responses to the other 4 stimuli. In contrast, Figure 3 shows a participant with particularly high sensitivity when deep breathing (sixth dashed line). This heterogeneity was present among all groups.

Figure 2 Source: Authors

Figure 3 Source: Authors

Patients belonging to group 2 frequently presented an enlarged DBF. In the top graph of Figure 4, notice the gap between the baseline of the feet. This difference is not present in the tonic component (middle graph) as the algorithm receives as input the Z-transform of the EDA signal.

Figure 4 Source: Authors

With respect to group 3, the differences in the EDA behavior are noticeable at plain sight. Figure 5 reports a subject with diminished M-SCR, but preserved M-SCL. Figure 6 illustrates abnormality in both metrics. More specifically, the signal starting at 0,7 µS contains no visible SCRs regardless of the 7 stimuli (top graph). Notice as well the estimated phasic component losing its sparsity as a result of the abnormality.

Figure 5 Source: Authors

Figure 6 Source: Authors

As shown in Table 1, diabetic participants reported both a diminished M-SCL and M-SCR when compared to the reference group. Numerically, group 2 had in average a 351% lower M-SCL and 55% lower M-SCR to stimuli. Additionally, group 3 presented 236% and 79% lower numeric values for these same two groups of variables.

Table 1

Characteristics	Reference group (n = 5)	Diabetics out of risk ( n = 6)	Diabetics at risk (n = 11)
Age	42,80 ± 18,34	54,33 ± 18,37	63,45 ± 8,72
TWD	0 ± 0	4,40 ± 4,43	11,64 ± 7,66
M-SCL	-0,66 ± 0,55	-2,32 ± 4,74	-1,56 ± 0,74
M-SCR	0,73 ± 0,36	0,33 ± 0,37	0,15 ± 0,08
DBF	0,23 ± 0,32	1,50 ± 3,10	1,66 ± 1,63

Abbreviations: TWD, time with diabetes; MSCL, mean skin conductance level; MSCR, mean skin conductance response; DBF, difference between feet. Age and TWD are in years, the rest in arbitrary units.

Significant difference was found for M-SCR between groups 1 and 3 (0,73 ± 0,36 vs 1,66 ± 1,63, p < 0,01). M-SCLs of groups 2 and 3 were significantly different (-2,32 ± 4,74 vs -1,56 ± 0,74, p < 0,05). DBF was significantly different between groups 1 and 3 (0,23 ± 0,32 vs 1,66 ± 1,63, p < 0,05). No other significant differences were found. Box plots illustrating the distributions for each group of M-SCL, M-SCR and DBF are presented in Figures 7, 8 and 9, respectively.

Figure 7 Source: Authors

Figure 8 Source: Authors

Figure 9 Source: Authors

Several stimuli techniques have been assessed in scientific literature (Deanfield et al. 1980, Khalfallah et al. 2012, Nazhel, Yetkin, Irkec and Kocer 2002, Rivera Farina et al. 2009, Vlckova et al. 2016, Wang et al. 2008). Most of them have come to the same conclusion: skin conductance does differ regarding autonomic neuropathy diagnose. For instance, a correlation was found between ulcerations in the feet and the voltage amplitude of the response to startle reflexes (Deanfield et al. 1980). In another research, current through the skin was induced and measured by means of electrochemical reactions (Khalfallah et al. 2012). Their investigation lead to a positive correlation between EDA and AN. In a study by Wang et al. (2008), diabetic patients were associated with higher latency between initiation of the stimulus and waves N and P. However, other study by Vlckova et al. (2016) did not associate lower voltage amplitude nor longer latency with patients diagnosed either with autonomic neuropathy or polyneuropathy.

Although EDA has been extensively studied due to its non-invasive qualities, it only informs about the direct current (DC) response of the body. Its alternating current (AC) counterpart is called bio-impedance analysis (BIA). Phase angles measured at different frequencies using the BIA proved to be useful features when classifying diabetes mellitus in age and sex-matched groups (Jun et al., 2018). For the same group, however, EDA showed marginal results (S. Kim et al., 2019). Compared to this study, measurement protocol of the EDA included both feet and hands, lasted 5 minutes and was repeated 30, 60, 90 and 120 minutes after meal intake. While taking into account glucose variation, subjects were asked to stand still and no intended stimuli were presented during each 5-minutes recording session. As such, the recorded data may be the combination of SCL information and spurious non-specific SCRs, making them highly variable. Mohanraj and Narayanan (2016) studied males with type 2 diabetes during the change from a supine to a standing position. In this study, significantly different EDA was found between diabetics and age and sex-matched controls. In line with our results, studies show that M-SCL by itself is not enough to discriminate between the control group and either of the two other groups (p > 0,05) here described. Statistical difference exists only when event-related SCRs are included.

To the best of our knowledge, researches fail to evaluate at once all kinds of stimulation and to assess external variables, since EDA is shaped by multiple other factors such as emotions, alertness and distress. There are several potential reasons for researchers failing to agree on the correlation between EDA, diabetes and autonomic neuropathy. Studies do not take into account the state of alertness, fail to capture the attention of the participant during the whole experiment or do not stimulate the subject at all. Therefore, emotional factors concerning or not the experiment might get involved (e.g. stress due to a possible bad outcome or boredom). Moreover, reasons have been given for one or another type of stimulation. Those in favor of intrinsic ones (e.g. Valsava Maneuver or deep breathing) argue that secondary health issues appear if the stimulus comes from outside, such as deficiencies in the autonomous efferent pathways (Rivera Farina et al. 2009). Thus, it was of paramount priority to conduct a test capable of gathering data regarding all the aforementioned stimuli under a single experiment.

One possible explanation for the variation in the 7 stimuli is the natural consequences of aging. It is well known that diabetes prevails among the elderly, and even though patients were asked to use glasses, and the volume of the computer was adjusted for those with reported hearing loss, decay in reflexes may affect the intended outcome of the stimulus. Similarly, elderly participants might be less prone to be stimulated by videos or crude scenes. While younger participants may become less agitated when asked to take a deep breath or to cough, and more aroused when disturbed by an unexpected loud noise.

A portable low-cost custom device for the acquisition of EDA was developed. It was then used to study the behavior of EDA and an experimental protocol accounting for a variety of stimuli was designed. This may allow health centers and clinics to apply an early screening where known but costly tests cannot be afforded, either because they are disposable and thus become costly for large populations, or because they are too expensive for the available budget.

A total of 22 subjects participated in a preliminary study to test the device. Lower M-SCR and higher DBF were associated with patients classified as at risk of AN. Lower M-SCL may not be directly associated with AN, although it could be taken into account when analyzed by a doctor. Finally, the abnormal state of only one of the above variables might indicate the beginning of AN.